Why triangular membership functions?
Fuzzy Sets and Systems
Sampling, fuzzy discretization, and signal reconstruction
Fuzzy Sets and Systems
Interfaces of fuzzy models: a study in fuzzy information processing
Information Sciences: an International Journal
Towards neuro-linguistic modeling: constraints for optimization of membership functions
Fuzzy Sets and Systems
Fuzzy Sets Engineering
A fuzzy c-means variant for the generation of fuzzy term sets
Fuzzy Sets and Systems - Theme: Modeling and learning
Design of transparent mamdani fuzzy inference systems
Design and application of hybrid intelligent systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A design methodology for fuzzy system interfaces
IEEE Transactions on Fuzzy Systems
The shape of fuzzy sets in adaptive function approximation
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.00 |
In this paper we address the issue of designing optimal fuzzy interfaces, which are fundamental components of a fuzzy inference system. Due to the different roles of input and output interfaces, optimality conditions are analyzed separately for the two types of interface. We prove that input interfaces are optimal when based on a particular class of fuzzy sets called ''bi-monotonic'', provided that mild conditions hold. The class of bi-monotonic fuzzy sets covers a broad range of fuzzy sets shapes, including convex fuzzy sets, so that the provided theoretical results can be applied to several fuzzy models. Such theoretical results are not applicable to output interfaces, for which a different optimality criterion is proposed. Such criterion leads to the definition of an optimality degree that measures the quality of a fuzzy output interface. Illustrative examples are presented to highlight the features of the proposed optimality degree in assessing the quality of output interfaces.